Visualization-Driven Illumination for Density Plots

TL;DR

This paper introduces a new visualization-driven illumination model for density plots that enhances the visibility of structures and outliers in large datasets while avoiding color artifacts, improving data analysis.

Contribution

The work presents a novel illumination model and image composition technique tailored for density plots, supporting detailed analysis of high- and medium-density regions and outliers.

Findings

Effective visualization of large datasets with up to two million points.

Improved detection of structures and outliers in density plots.

Reduction of color distortion and artifacts in density visualization.

Abstract

We present a novel visualization-driven illumination model for density plots, a new technique to enhance density plots by effectively revealing the detailed structures in high- and medium-density regions and outliers in low-density regions, while avoiding artifacts in the density field's colors. When visualizing large and dense discrete point samples, scatterplots and dot density maps often suffer from overplotting, and density plots are commonly employed to provide aggregated views while revealing underlying structures. Yet, in such density plots, existing illumination models may produce color distortion and hide details in low-density regions, making it challenging to look up density values, compare them, and find outliers. The key novelty in this work includes (i) a visualization-driven illumination model that inherently supports density-plot-specific analysis tasks and (ii) a new image composition technique to reduce the interference between the image shading and the color-encoded density values. To demonstrate the effectiveness of our technique, we conducted a quantitative study, an empirical evaluation of our technique in a controlled study, and two case studies, exploring twelve datasets with up to two million data point samples.